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Main Title Dynamic models in biology /
Author Ellner, Stephen P.,
Other Authors
Author Title of a Work
Guckenheimer, John.
Publisher Princeton University Press,
Year Published 2006
OCLC Number 63398363
ISBN 0691118434; 9780691118437; 9780691125893; 0691125899
Subjects Biology--Mathematical models ; Models, Biological ; Biology--methods ; Computational Biology ; Disease Transmission, Infectious--statistics & numerical data ; Population Dynamics ; Biologie--(DE-588)4006851-1 ; Dynamisches Modell--(DE-588)4150932-8 ; Disease Transmission--statistics & numerical data
Internet Access
Description Access URL
Contributor biographical information
Publisher description
Table of contents
Table of contents
Library Call Number Additional Info Location Last
EKCM  QH323.5.E44 2006 CEMM/GEMMD Library/Gulf Breeze,FL 08/31/2007
Collation xxii, 329 pages : illustrations ; 26 cm
Includes bibliographical references and index.
Contents Notes
What are dynamic models? -- Matrix models and structured population dynamics -- Membrane channels and action potentials -- Cellular dynamics : pathways of gene expression -- Dynamical systems -- Differential equation models for infectious disease -- Spatial patterns in biology -- Agent-based and other computational models for complex systems -- Building dynamic models. From controlling disease outbreaks to predicting heart attacks, dynamic models are increasingly crucial for understanding biological processes. Many universities are starting undergraduate programs in computational biology to introduce students to this rapidly growing field. In Dynamic Models in Biology, the first text on dynamic models specifically written for undergraduate students in the biological sciences, ecologist Stephen Ellner and mathematician John Guckenheimer teach students how to understand, build, and use dynamic models in biology. Developed from a course taught by Ellner and Guckenheimer at Cornell University, the book is organized around biological applications, with mathematics and computing developed through case studies at the molecular, cellular, and population levels. The authors cover both simple analytic models--the sort usually found in mathematical biology texts--and the complex computational models now used by both biologists and mathematicians.